from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.text.all import *



# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "mahernher/Modelo_Tweet_Topic_Single"

learner = from_pretrained_fastai(repo_id)
labels = ["arts_&_culture","business_&_entrepreneurs","pop_culture","daily_life","sports_&_gaming","science_&_technology"]

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(text):
    #img = PILImage.create(img)
    pred,pred_idx,probs = learner.predict(text)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Label(num_top_classes=3),examples=['Massive WELL DONE to BSLFC Reserves today in their Friendly winning a smashing 15-0. Goals and assists: Stacey @AbiRigler ⚽️⚽️⚽️ Sam ⚽️ Chelsea ⚽️⚽️ Rocket ⚽️ {{USERNAME}} ⚽️ Lauren ⚽️⚽️ Becky ⚽️⚽️ Doxa ⚽️ Kim ⚽️ Debs ⚽️ LEAGUE GAME NEXT WEEK {@Hampshire FA@}']).launch(share=False)